Prediction of N2O emissions under different field management practices and climate conditions.
Prediction of N2O emissions under different field management practices and climate conditions.
复制标题
DOI：
10.1016/j.scitotenv.2018.07.364
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Foltz;J. Zilles;S. Koloutsou-Vakakis
中科院分区：
文献类型：
--
作者：
M. Foltz;J. Zilles;S. Koloutsou-Vakakis
由于一氧化二氮（N2O）对全球气候变化和平流层臭氧破坏的贡献，了解气候和农业管理如何影响N2O排放是很重要的。虽然基于过程的反硝化分解（DNDC）模型通常用于量化N2O的排放量，但这些预测的准确性仍然存在问题，并且不清楚哪些输入变量，环境或现场管理，对模型性能的影响最大。在这项研究中，DNDC的N2O通量的预测进行了评估，从两个气候不同的玉米田网站在美国（科罗拉多灌溉田和明尼苏达州灌溉田）。除了气候，这些地点提供了额外的优势，测量可用于多种田间管理实践，包括施肥，耕作和作物轮作。这项评估发现，DNDC没有一致，正确地预测每日规模的N2O通量。累积生长季N2O通量显着低于预测在科罗拉多和明尼苏达州的下和过度预测。四个土壤输入参数的模型校准没有显着提高N2O排放预测在任何地点或时间尺度。模拟和测量的N2O通量和模型误差都与降水密切相关。N2O通量的过度预测与强降水和高模拟反硝化作用。根据我们的研究结果，在温带气候区的玉米种植系统的模型改进，以减少模型误差应集中在更好地解释降水对反硝化作用的影响。尽管每日和累积生长季节N2O通量的差异，DNDC正确地确定了唯一的现场管理（施肥量），显着影响测得的N2O通量。
Due to the contributions of nitrous oxide (N2O) to global climate change and stratospheric ozone destruction, it is important to understand how climate and agricultural management affect N2O emissions. Although the process-based Denitrification Decomposition (DNDC) model is often used for quantifying emissions of N2O, the accuracy of these predictions remains in question, and it is not clear which input variables, environmental or field management, have the greatest effect on model performance. In this study, DNDC was evaluated for prediction of N2O fluxes from two climatically-different corn-field sites in the United States (a Colorado irrigated field and a Minnesota rainfed field). Besides climate, these sites offer the additional advantage that measurements are available for multiple field management practices, including fertilizer application, tillage, and crop rotation. This evaluation found that DNDC did not consistently, correctly predict daily-scale N2O fluxes. Cumulative growing season N2O fluxes were significantly under-predicted in Colorado and were both under- and over-predicted in Minnesota. Model calibration of four soil input parameters did not significantly improve N2O emission predictions at either site or time scale. Modeled and measured N2O fluxes and model error were all strongly correlated with precipitation. Over-predictions of N2O fluxes were associated with heavy precipitation and high modeled denitrification. Based on our results, model improvements to decrease model error for corn cropping systems in temperate climate zones should focus on better accounting for the effects of precipitation on denitrification. Despite discrepancies in daily and cumulative growing season N2O fluxes, DNDC correctly identified the only field management (fertilizer application rate) that significantly influenced the measured N2O fluxes.